Source code for optuna.visualization.matplotlib._intermediate_values

from optuna._experimental import experimental_func
from optuna.logging import get_logger
from import Study
from optuna.visualization._intermediate_values import _get_intermediate_plot_info
from optuna.visualization._intermediate_values import _IntermediatePlotInfo
from optuna.visualization.matplotlib._matplotlib_imports import _imports

if _imports.is_successful():
    from optuna.visualization.matplotlib._matplotlib_imports import Axes
    from optuna.visualization.matplotlib._matplotlib_imports import plt

_logger = get_logger(__name__)

[docs]@experimental_func("2.2.0") def plot_intermediate_values(study: Study) -> "Axes": """Plot intermediate values of all trials in a study with Matplotlib. .. note:: Please refer to `matplotlib.pyplot.legend <>`_ to adjust the style of the generated legend. Example: The following code snippet shows how to plot intermediate values. .. plot:: import optuna def f(x): return (x - 2) ** 2 def df(x): return 2 * x - 4 def objective(trial): lr = trial.suggest_float("lr", 1e-5, 1e-1, log=True) x = 3 for step in range(128): y = f(x), step=step) if trial.should_prune(): raise optuna.TrialPruned() gy = df(x) x -= gy * lr return y sampler = optuna.samplers.TPESampler(seed=10) study = optuna.create_study(sampler=sampler) study.optimize(objective, n_trials=16) optuna.visualization.matplotlib.plot_intermediate_values(study) .. seealso:: Please refer to :func:`optuna.visualization.plot_intermediate_values` for an example. Args: study: A :class:`` object whose trials are plotted for their intermediate values. Returns: A :class:`matplotlib.axes.Axes` object. """ _imports.check() return _get_intermediate_plot(_get_intermediate_plot_info(study))
def _get_intermediate_plot(info: _IntermediatePlotInfo) -> "Axes": # Set up the graph style."ggplot") # Use ggplot style sheet for similar outputs to plotly. _, ax = plt.subplots(tight_layout=True) ax.set_title("Intermediate Values Plot") ax.set_xlabel("Step") ax.set_ylabel("Intermediate Value") cmap = plt.get_cmap("tab20") # Use tab20 colormap for multiple line plots. trial_infos = info.trial_infos for i, tinfo in enumerate(trial_infos): ax.plot( tuple((x for x, _ in tinfo.sorted_intermediate_values)), tuple((y for _, y in tinfo.sorted_intermediate_values)), color=cmap(i), marker=".", alpha=0.7, label="Trial{}".format(tinfo.trial_number), ) if len(trial_infos) >= 2: ax.legend(bbox_to_anchor=(1.05, 1), loc="upper left", borderaxespad=0.0) return ax